no code implementations • 21 Mar 2024 • Haoran Hou, Mingtao Feng, Zijie Wu, Weisheng Dong, Qing Zhu, Yaonan Wang, Ajmal Mian
In this work, we focus on the distributional properties of point clouds and formulate the voting process as generating new points in the high-density region of the distribution of object centers.
no code implementations • 31 Dec 2023 • Run Shao, Cheng Yang, Qiujun Li, Qing Zhu, Yongjun Zhang, Yansheng Li, Yu Liu, Yong Tang, Dapeng Liu, Shizhong Yang, Haifeng Li
We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities.
no code implementations • 5 Dec 2023 • Sandeep Chinta, Xiang Gao, Qing Zhu
One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport.
no code implementations • 6 Oct 2023 • Qing Zhu, Qirong Mao, Jialin Zhang, Xiaohua Huang, Wenming Zheng
Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene.
1 code implementation • 23 Aug 2023 • Peifeng Ma, Li Chen, Chang Yu, Qing Zhu, Yulin Ding
The chosen study area is Lantau Island, Hong Kong, where we conducted a comprehensive dynamic LSA spanning from 1992 to 2019.
no code implementations • 5 Aug 2023 • TianXing Li, Rui Shi, Qing Zhu, Takashi Kanai
Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details.
no code implementations • 7 Jun 2023 • Libin Wang, Han Hu, Qisen Shang, Bo Xu, Qing Zhu
The lack of fa\c{c}ade structures in photogrammetric mesh models renders them inadequate for meeting the demands of intricate applications.
1 code implementation • 14 Apr 2023 • Cheng Liao, Han Hu, Xuekun Yuan, Haifeng Li, Chao Liu, Chunyang Liu, Gui Fu, Yulin Ding, Qing Zhu
This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings.
no code implementations • 12 Apr 2023 • Haojia Yu, Han Hu, Bo Xu, Qisen Shang, Zhendong Wang, Qing Zhu
Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images.
no code implementations • 30 Mar 2023 • Qisen Shang, Han Hu, Haojia Yu, Bo Xu, Libin Wang, Qing Zhu
Experimental results on publicly available fa\c{c}ade image and 3D model datasets demonstrate that our method yields superior results and effectively addresses issues associated with flawed textures.
no code implementations • 15 Nov 2022 • Chao Tao, Ji Qi, Mingning Guo, Qing Zhu, Haifeng Li
Deep learning has achieved great success in learning features from massive remote sensing images (RSIs).
1 code implementation • 10 Apr 2022 • Chao Tao, Ji Qia, Guo Zhang, Qing Zhu, Weipeng Lu, Haifeng Li
We believe that a general model which is trained by a label-free and task-independent way may be the next paradigm for RSIU and hope the insights distilled from this study can help to foster the development of an original vision model for RSIU.
no code implementations • 22 Jan 2022 • Han Hu, Xinrong Liang, Yulin Ding, Qisen Shang, Bo Xu, Xuming Ge, Min Chen, Ruofei Zhong, Qing Zhu
Unfortunately, the large amount of interactive sample labeling efforts has dramatically hindered the application of deep learning methods, especially for 3D modeling tasks, which require heterogeneous samples.
no code implementations • 23 Nov 2021 • Yifan Chang, Wenbo Li, Jian Peng, Bo Tang, Yu Kang, Yinjie Lei, Yuanmiao Gui, Qing Zhu, Yu Liu, Haifeng Li
Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism.
1 code implementation • 3 Oct 2021 • Li Chen, Yulin Ding, Saeid Pirasteh, Han Hu, Qing Zhu, Haowei Zeng, Haojia Yu, Qisen Shang, Yongfei Song
Then, the critical problem is that in each block with limited samples, conducting training and testing a model is impossible for a satisfactory LSM prediction, especially in dangerous mountainous areas where landslide surveying is expensive.
no code implementations • 30 Jun 2021 • Haifeng Li, Jun Cao, Jiawei Zhu, Yu Liu, Qing Zhu, Guohua Wu
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
1 code implementation • 20 Jun 2021 • Haifeng Li, Yi Li, Guo Zhang, Ruoyun Liu, Haozhe Huang, Qing Zhu, Chao Tao
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.
no code implementations • 2 Mar 2021 • Haifeng Li, Jun Cao, Jiawei Zhu, Qing Zhu, Guohua Wu
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
no code implementations • 26 Nov 2020 • Qing Zhu, Shengzhi Huang, Han Hu, Haifeng Li, Min Chen, Ruofei Zhong
Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings.
1 code implementation • 23 Nov 2020 • Qing Zhu, Qisen Shang, Han Hu, Haojia Yu, Ruofei Zhong
Finally, the completed rendered image is deintegrated to the original texture atlas and the triangles for the vehicles are also flattened for improved meshes.
no code implementations • 11 Jun 2020 • Zijie Wu, Yaonan Wang, Qing Zhu, Jianxu Mao, Haotian Wu, Mingtao Feng, Ajmal Mian
Different from the most existing point set registration methods which usually extract the descriptors to find correspondences between point sets, our proposed MPE alignment method is able to handle large scale raw data offset without depending on traditional descriptors extraction, whether for the local or global registration methods.
1 code implementation • 21 Feb 2020 • Qing Zhu, Zhendong Wang, Han Hu, Linfu Xie, Xuming Ge, Yeting Zhang
Second, aerial models are rendered to the initial ground views, in which the color, depth and normal images are obtained.
1 code implementation • 20 Feb 2020 • Qing Zhu, Lin Chen, Han Hu, Binzhi Xu, Yeting Zhang, Haifeng Li
The second uses a scale attention mechanism to guide the up-sampling of features from the coarse level by a learned weight map.
1 code implementation • 20 Feb 2020 • Han Hu, Libin Wang, Mier Zhang, Yulin Ding, Qing Zhu
Regularized arrangement of primitives on building fa\c{c}ades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment.
1 code implementation • 26 Oct 2019 • Qing Zhu, Cheng Liao, Han Hu, Xiaoming Mei, Haifeng Li
This paper proposes a novel multi attending path neural network (MAP-Net) for accurately extracting multiscale building footprints and precise boundaries.
no code implementations • 20 Jun 2019 • Jonathan Li, Rongren Wu, Yiping Chen, Qing Zhu, Zhipeng Luo, Cheng Wang
Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network, PointNLM, to semantically segment tree crowns.
1 code implementation • 4 Dec 2018 • Jian Peng, Jiang Hao, Zhuo Li, Enqiang Guo, Xiaohong Wan, Deng Min, Qing Zhu, Haifeng Li
In this paper, we proposed a Soft Parameters Pruning (SPP) strategy to reach the trade-off between short-term and long-term profit of a learning model by freeing those parameters less contributing to remember former task domain knowledge to learn future tasks, and preserving memories about previous tasks via those parameters effectively encoding knowledge about tasks at the same time.
2 code implementations • 22 Oct 2018 • Enqiang Guo, Xinsha Fu, Jiawei Zhu, Min Deng, Yu Liu, Qing Zhu, Haifeng Li
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
no code implementations • 19 Aug 2018 • Yuanxin Ye, Lorenzo Bruzzone, Jie Shan, Francesca Bovolo, Qing Zhu
To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration.